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InsurTalks Podcast with Horacio Sanchez-Granel: Can Insurers respond swiftly to customer needs during a Pandemic?

7 minutes, 50 seconds read

According to the International Monetary Fund (IMF), the global economy is expected to shrink by over 3 percent in 2020 – the steepest slowdown since the Great Depression of the 1930s. To understand the impact of the COVID crisis in the Latin American Insurance Industry, we interviewed Mr. Horacio Sanchez- Granel from Buenos Aires, Argentina, and Insurance & Reinsurance Consultant.

Mr. Horacio has been Chairman and CEO of Boston Seguros, P&C and Life Insurance company for 21 years. Previously, he held senior executive positions in three insurance companies and has several years of experience managing financial service institutions. He has also held other executive positions in an Argentinian oil company and a tractor and industrial machinery international company. Currently, he is a Board Member and part of the Executive Committee in Nacion Seguros, the state-owned insurance company. He also works as an insurance and reinsurance consultant for Argentine and Latin American markets.

Connect with Mr. Horacio – LinkedIn

The excerpt from the interview:

Customer Relationship during the pandemic

Insurance companies play a pivotal role during times of economic stress by helping companies and individuals manage risks and cushion against losses. How should Insurers respond to their customer’s needs, especially since there will be scrutiny about how they respond during this critical time — and it will dictate public perception for many years to come?

Mr. Horacio: Insurers must safeguard the interests of their clients and advise them on the scope of the coverage. They must communicate that the coverage is not infinite but rather has limits in terms of the risks covered, amount insured, and the origin of the claim. There are doubtful cases but insurers should be flexible enough and protect their client from damages. It was not possible to predict COVID-19. Both Life and P&Cs have been affected and have huge arrears to be paid. 

On the other hand, claims processes need to be more transparent. They should adhere to the compliances of the insurance companies. In these times, selling agents and insurance brokers should be more flexible and build close relationships with the clients. They should explain to them the possibility of the claim they are trying to reimburse. In Latin America, we don’t have many claims related to business interruption. That coverage is not very common here. 

Especially in Argentina, businesses have slowed down due to lockdowns. The claims ratio in this area is going down but claims in life insurance policies have increased a bit. However, the impact here is not as big as the USA or Europe.  

Business Continuity in the time of Pandemics

What are some new business models that Insurance Carriers are considering to meet the expectations of life in ‘The New Normal’? More specifically, where is the new business going to come from, for Insurance, over the next two years?

Mr. Horacio: The outbreak of the COVID-19 pandemic has changed the dynamics of work culture, human relationships, and daily routines. Many companies including insurers are adopting digital solutions within their operations. Organizations are reimagining their business models to adapt to new paradigms to be more sustainable and profitable. 

The New Normal has given rise to new coverages in various insurance lines to cover risks originated by this pandemic or any possible future pandemics. 

For example, new clauses such as Loss of Profit due to business interruption and pandemics in Life and Health insurance, and worker’s compensation will now be included in the respective policies. Interruption of business processes entails new set-up and investment. Some other new coverages will also be introduced such as the cost of maintenance due to the non-use of offices, premises, or industrial facilities. Cyber Insurance will now be a must as most of the workforce is working remotely. The rate of cybercrimes was much higher in developed countries before the pandemic, but now even the developing countries are at risk. This will accelerate the need for Cyber Insurance in developing countries. 

Road to recovery

Many General Insurance lines are hit- Travel, Motor, Home – what will be the road to recovery for these Insurance lines?

Mr. Horacio: Since people are avoiding travel altogether, the travel and motor insurance industry are hit badly. Many customers in Argentina are asking Insurance companies to give some discount on the premiums. We will see some big changes in these insurance lines. Going forward, on-demand or pay-as-you-use policies will prevail more in these insurance lines. 

Role of AI in pandemic crisis management

Before the Pandemic crisis began, technologies like AI have been instrumental in modernizing the business of insurance and advancing their digital transformation. Where are some of the biggest gaps being exposed to insurance organizations, and How is technology going to solve these problems?

Mr. Horacio: Going forward, AI along with IoT and other technologies will play a crucial role in the Insurance industry as a whole. They will rely on statistical analysis of large databases to predict future behaviors. The new challenge is how to incorporate unknown risks into the existing models to be able to properly underwrite and price risks, anticipate client behavior, facilitate complex operating processes, manage complex claims and detect possible frauds.

Financial assets are the main asset of an insurance company. They are under the influence of the volatility of financial markets. Technology here can help by analyzing different scenarios but the ultimate decision is in the hands of the banks. 

Challenges & opportunities in adoption of AI

Why Insurers hesitate to invest in AI?

Mr. Horacio:  Companies were investing in technology earlier, but now it has accelerated due to the unprecedented change brought about by COVID-19 pandemic. Not just the developed countries but in developing countries such as Latin America, I see a big wave of new investments in technology. Technology companies are also looking forward to this change. Insurers will eventually overcome their hesitation and invest more in AI and other technologies. 

[Related: 5 Challenges in AI implementation for Insurers]

Which area will see max Investment in AI- claims, underwriting, fraud detection, marketing in Argentina, and Latin American Insurance markets?

Mr. Horacio: Before the outbreak of the COVID-19 pandemic, investment in AI was more targeted towards claims, fraud, underwriting, back-office operations, etc. Going forward, predicting future scenarios will be a challenge. Historical data might not be useful here. Therefore, in the New Normal, all aspects of an Insurance company will have to be developed under the umbrella of AI.

Product Innovation

Consumers, now more than ever are seeking value-added experiences with the products & services they buy. How will these expectations amidst this Pandemic backdrop impact new product innovation within insurance? 

Mr. Horacio: Customers want a more palpable relationship with their insurers. Customer Experience is going to be a fundamental aspect during the purchase of insurance coverage. In addition to being simple, the purchase and subscription process will have to be perceived as a service that accompanies the client at every moment they need it. These additions will help insurers gain more information on their customer’s actions and behavior. Based on this data, they can dynamically adapt the coverage and pricing of the product. I call it — Dynamic-on-demand coverage. 

Challenges in Latin American Insurance industry

What are some of the technological challenges faced by Insurers in Argentina and Latin American markets operating in the New Normal? 

Mr. Horacio: Insurance industry for many years has been static but now is moving forward in many ways. The world including the Latin American Insurance is witnessing rapid development in terms of technology. The InsurTech industry is parallel to the Insurance industry. It aids in the development of the insurance companies. The whole world of insurance is making advances in technology. Different economics have different buying patterns for insurance products. One such insurance product that should develop is Microinsurance

Microinsurance needs technology, without which it is very difficult to manage. In a sense, the outbreak of COVID-19 was beneficial in accelerating these technological developments.   

[Related – AI can help bridge customer gaps for microinsurers]

Insurance buying behavior in the post-pandemic world

In a post-pandemic World, will insurance ever be bought offline? Or have we crossed the threshold for now buying policies purely online? 

Mr. Horacio:  Personal line insurances such as car, accident, personal, travel, microinsurance are mostly purchased online. In Argentina, 60% of the insurance policies are sold by traditional marketing and sales through brokers. However, in commercial, industrial, energy, transport, and large companies in general, the marketing and sales will continue the traditional ways but through electronic means. The use of IoT, sensors, drones dynamically monitoring the facilities and processes in different industries is increasing. Argentina, which is an agro-based country already has technologies such as drones and IoT which monitor the crops in place. AI will surely be crucial here to analyze the data and enable quick decisions in case of a fire or an accident.  

Wrapping up

Summing up — Mr. Horacio Sanchez-Granel shared valuable insights on the challenges in the Latin American Insurance Industry, how AI technologies can aid in policymaking and rise in dynamic-on-demand policies in the post-pandemic world.

AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us at hello@mantralabsglobal.com.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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